Energy Efficiency and Fuel Economy Analysis of a Parallel Hybrid Electric Bus in Different Chinese Urban Driving Cycles

Transcription

1 EVS28 KINTEX, Korea, May 3-6, 205 Energy Efficiency and Fuel Economy Analysis of a Parallel Hybrid Electric Bus in Different Chinese Urban Driving Cycles Xiaogang Wu, Jingfu Chen,2 Jiuyu Du 2 Harbin University of Science and Technology, Harbin, China 2 State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 00084, China, dujiuyu@tsinghua.edu.cn Abstract Against a kind of parallel hybrid electric bus which is demonstrated in a city of China, a simulation model of the parallel hybrid powertrain system is established by referring to parameters of the demonstrating bus. Regarding the Chinese typical city bus driving cycle and the bus driving cycle of Harbin City, Heilongjiang Province as the driving cycles for the simulation, the fuel economy of the parallel hybrid powertrain system is analysed by using a method of the energy flow diagram. The simulation results reveal that the rate of fuel saving of the hybrid powertrain system is different under different driving cycles. It can reach 2.2% in the Chinese typical city bus driving cycle and 22.69% in the bus driving cycle of Harbin City, Heilongjiang Province. The fuel economy of the powertrain system can be optimized by improving motor and its controller, energy storage devices and other components, or the control method. Keywords: Parallel hybrid powertrain, Driving cycles, Fuel economy, Energy efficiency, simulation model Introduction In the parallel hybrid powertrain system, the driving force is usually provided by the engine, and the motor plays a supporting role. Even though the motor does not run, the system can still drive the vehicle by the engine. The function of the motor just changes the working state of the engine, which keeps the engine from working at the inefficient area and improves the overall efficiency of the system. Therefore, the demand power of the motor and engine can be reduced, and the battery capacity and the quality of the battery pack can be lower. Those factors can let the manufacturing cost down []. Y. Zhang presents a design method of an energy control strategy for parallel hybrid electric vehicles by using fuzzy multi-objective optimization. And compared with the conventional rule-based control strategy and fuzzy logical control strategy, the proposed fuzzy multiobjective control strategy not only improves fuel economy and emission level but also maintains the battery state of charge within its operation range effectively [2]. Mitra Pourabdollah introduces a novel method for the simultaneous optimization of energy management and powertrain component sizing of a parallel plug-in hybrid electric vehicle. The method allows the study of the effect of some performance requirements, i.e., acceleration, top speed, and all-electric range, on the component sizes and total cost [3]. Jong-Seob Won represents an intelligent energy management agent (IEMA) for parallel hybrid vehicles, that energy management strategies for the torque distribution and charge sustenance tasks are established and EVS28 International Electric Vehicle Symposium and Exhibition

2 implemented [4]. Lianghong Wu describes the application of a novel multi-objective selfadaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles. The optimization problem is formulated as a MOP with competing objectives of FC, CO emission, HC emission, and NOx emission. The optimization is performed over three driving cycles [5]. Saida Kermani presents a method, that a real-time experiment using the power-hardware-in-theloop (PHIL) principle. The measured fuel consumption and the obtained battery SOC trajectory demonstrate good performance of the proposed control [6]. Lin He presents a torquedemand-based control approach for parallel hybrid powertrains that consist of a torque distributor, a load observer, and two feedback control loops for an internal combustion engine and an electric motor, respectively [7]. Datong Qing et al. present a control strategy by combining the threshold values and instantaneous optimization for Plug-in parallel single-motor hybrid vehicles. While conducting the simulation comparison, it calculated the effect of energy price ratio parameters on the energy economy [8]. Yuhui Hu et al. introduce a reverse test analytic method of a shifting schedule by a uniaxial parallel hybrid bus [9]. This paper regards a parallel hybrid electric bus demonstrating in a city of China as the research object, establishes a simulation model of the parallel hybrid powertrain system, regards the Chinese typical city bus driving cycle and the bus driving cycle of Harbin City, Heilongjiang Province as the driving cycles for the simulation, uses a method of the energy flow diagram, and conducts the comparative analysis for the fuel economy of the parallel hybrid powertrain system. Then it evaluates the fuel efficiency of the parallel hybrid electric bus under the Chinese urban driving cycles. 2 Configuration analysis of the parallel hybrid powertrain system The configuration of the parallel hybrid electric bus is shown in Figure. The system is hybrid after the clutch. It consists of the engine, the motor, energy storage elements and the mechanical transmission of conventional vehicles. The motor and the engine mix the mechanical energy by mechanically coupling to drive the vehicle. In the system, the motor has two kinds of working modes: electric motor / generator, which not only converts the energy stored in the battery to the mechanical energy to output, but also converts the mechanical energy to the energy to store in the battery by the generator mode. Figure : Configuration of the parallel hybrid powertrain The work of the parallel hybrid powertrain system is mainly three modes: Pure electric, Cooperation and Braking energy recovery. 2. Pure electric mode The pure electric mode of the parallel hybrid powertrain system is shown in Figure 2. When the vehicle starts, runs at the low speed or reverses, the clutch releases. The engine does not work. The motor provides the driving force for the vehicle. And the battery discharges. This model makes the engine stop working completely in the low load area and reduces the fuel consumption. Discharge Figure 2: Pure electric mode of the Parallel hybrid powertrain 2.2 Cooperation mode The cooperation mode of the parallel hybrid powertrain system is shown in Figure 3. When the vehicle drives normally, the clutch is in a combined state. The engine and the motor work cooperatively. When the vehicle starts acceleration or climbs slopes, the demand power of the vehicle is comparatively large. The motor outputs the torque in the electric model and conducts the power compensation to the system. And the battery EVS28 International Electric Vehicle Symposium and Exhibition 2

3 discharges. When the vehicle drives in low-speed cruising or the SOC of the battery is in a low state, the motor is in the generation mode and converts a part of the energy of engine to the electrical energy for battery charging. Combine Charge/ Discharge Generate/ Figure 3: Cooperation mode of the Parallel hybrid powertrain system 2.3 Braking energy recovery mode The braking energy recovery mode of the parallel hybrid powertrain system is shown in Figure 4. When the vehicle is braking, the motor is in the generation mode and converts the mechanical energy from the recovery to the electrical energy for the battery charging. The clutch releases. The engine is stopped, which can reduce the friction loss of the motored engine for more energy recovery. Stop Release Charge Generate Figure 4: Braking energy recovery mode of the Parallel hybrid powertrain system 3 Modelling parallel hybrid powertrain system According to the actual operation data of the parallel hybrid bus powertrain system and the modelling method in [0-], this paper establishes the model of parallel hybrid electric bus in MATLAB/Simulink. It consists of the engine model, the energy storage element model, the motor model, the transmission model and the vehicle dynamics model. Considering the high complexity of the diesel engine and the motor, the components had bench tests before the modelling. Then, the MAP diagrams can be produced according to the actual test results of the bench tests and be used to establish the simulation model. It reduces the complexity of the modelling by using the MAP diagrams instead of the complicated mathematical description. What s more, it improves the credibility by regarding the MAP diagrams from the test data as the simulation model. 3. Diesel engine model The diesel engine mainly meets the following formulas: neng n () r s eng e, eng T f n (2) C f n T (3) eng 2 eng, eng Where, f is the MAP diagram of the diesel engine throttle characteristics; f 2 is the MAP diagram of the diesel fuel consumption characteristics; n r is the target speed of the diesel engine, r/min; τ e is the time constant; α is the throttle signal, %; n eng is the speed of the diesel engine, r/min; T eng is the torque of the diesel engine, N m; C eng is the instantaneous fuel consumption of the engine, g/kwh. The MAP diagrams are all from the bench tests. 3.2 model The motor model consists of the MAP diagram of steady state efficiency characteristics and one order inertial link and can be expressed by the following formulas: η f n, T (4) T m m m m min T, T m r max max m2 m s m (5) T f n (6) where, f m is the MAP diagram of the motor efficiency; f m2 is the MAP diagram of the maximum output torque characteristics of motor; η m is the motor efficiency, %; n m is the motor speed; T m,t r,t max are the motor output torque, the target torque and the maximum torque respectively, N m; τ m is the time constant value. 3.3 model This paper refers to [2] to establish the super capacity model. The state equation is as follows: VC il VC CU Rp CU VL VC ESRiL (7) EVS28 International Electric Vehicle Symposium and Exhibition 3

4 where, i L is the input current; V L is the input voltage; ESR is the equivalent internal resistance; C U is the value of the super capacity. 3.4 system model The transmission system model mainly refers to the description of the relationship between the vehicle speed and the engine speed as well as the actual operation parameters of the parallel hybrid electric bus in [3]. In the paper, the transmission researched is a 5-speed manual transmission. The transmission system model is expressed by the following formulas: n 2.65 i i u / r (8) T motor 0 g a T a motor (9) 0 ii g T where, n motor is the motor speed, r/min; i 0 is the final drive ratio; i g is the transmission ratio; u a is the vehicle speed, km/h; r is the wheel radius, m; T motor is the output torque of the motor, N m; T a is the total drive torque on the wheels, N m; η T is the overall efficiency of the transmission system, %. Table : The mainly parameters of the parallel hybrid powertrain system bus in the simulation Vehicle size(long wide high)/mm Vehicle curb mass/kg 2500 Vehicle frontal area/m power/kw 8 Continuous motor power/kw 30 peak power/kw 60 Maximum torque motor/nm 800 Main reducer ratio i st gear ratio nd gear ratio.86 3rd gear ratio.4 4th gear ratio.0 5th gear ratio Vehicle dynamics model Road load characteristics are assumed as to be ideal, that: Air absolute speed is 0, and the cement pavement is well. When the vehicle travels on the pavement, the traction motor needs to overcome the driving resistance (F t ) including the rolling resistance (F f ), the air resistance (F w ), the slope resistance (F i ) and the acceleration resistance (F j ): Ft Ff Fw Fi F (0) j F fmgcos atani () f 2 Fw Cd Aua 2 (2) F mgsin atani (3) i dua Fj m (4) dt Ft 3.6 T Pmotor / u (5) a where, f is the rolling resistance coefficient; m is the bus mass, kg; g is the value of gravitational acceleration, m/s 2 ; i is the road slop, %; C d is the air resistance coefficient; A is the frontal area, m 2 ; ρ is the air density, kg/m 3 ; u a is the vehicle speed, km/h; δ is the conversion coefficient of the revolving mass; η T is the total efficiency of the drive system, %; P motor is the output power of the traction motor, W. The parameters of the parallel hybrid powertrain bus in the simulation are shown in Table. 4 Simulation results and analysis For analysing the fuel efficiency of the parallel hybrid electric bus in the Chinese urban driving cycles, this paper regards the Chinese typical city bus driving cycle and the bus driving cycle of Harbin, in Heilongjiang Province as the driving cycles and analyses energy efficiencies and the fuel efficiencies in different driving cycles. The driving cycles which are used in the simulation are shown in Figure 5. v/km/h t/s v/km/h (a) The Chinese typical city bus driving cycle t/s (b) The bus driving cycle of Harbin, in Heilongjiang Province Figure 5: Chinese urban driving cycles for the simulation EVS28 International Electric Vehicle Symposium and Exhibition 4

5 As shown in Table 2, eigenvalues of different driving cycles can be got from Figure 5. Table 2: Eigenvalues of different driving cycles for the simulation Performance statistics Chinese typical city bus driving cycle Bus driving cycle of Harbin, in Heilongjiang Province Cycle time/s Driving distance/km Top speed/km/h Average speed/km/h Maximum acceleration/m/s -2 Maximum deceleration/m/s -2 Average acceleration/m/s -2 Average deceleration /m/s -2 Idle rate/% For comparing the economic impact of parallel hybrid powertrain system, the simulation sets that the energy of the air conditioning, the power steering and other auxiliary devices are all from the engine. The average power is 5kW. The simulation also sets that the initial value of the SOC is the same with the termination value. 4. Energy efficiency of the Chinese typical city bus driving cycle According to the model of the parallel hybrid electric bus, the simulation ran in the Chinese typical city bus driving cycle. Figure 6 is the energy flow diagram of the parallel hybrid powertrain system which conducted in the Chinese typical city bus driving cycle. As can be seen from Figure 6, regarding the output power of driving the vehicle as the reference value, the theoretical braking energy can reach 5.29%. The efficiency of the engine can reach 35%. The driving and braking energy efficiencies of the motor and its controller section are 82.5% and 77.33%, respectively, which still has space to optimized control. 4.2 Energy Efficiency of the Bus Driving Cycle of Harbin City, Heilongjiang Province According to the model of the parallel hybrid electric bus, the simulation ran in the bus driving cycle of Harbin City, Heilongjiang Province. Figure 7 is the energy flow diagram of the parallel hybrid powertrain system which conducted in the bus driving cycle of Harbin City, Heilongjiang Province. Fuel % (34.4kWh) Fuel 42.22% (22.9kWh) 34% Diesel (Max 4.2%) 35% Diesel (Max 4.2%) 25.58% (.608kWh) % (22.532kWh) Loss 99% Loss 0.% (0.006kWh) 44.36% (7.77kWh) 2.87% (6.076kWh) 2.76% (6.07kWh) 83.45% (4.492kWh) 00% (5.383kWh) Vehicle Axle 3.48% (.695kWh) % (4.49kWh) 29.3% (.578kWh) Auxiliary and Controller Loss Devices Loss (5kW) -driven Loss Efficiency 3.5% 82.5% (0.89kWh) 6.55% (0.89kWh) 48.7% Generation (2.622kWh) Efficiency 77.33% 5.29% (2.76kWh) 20.06% Energy Storage (.08kWh) Battery System Loss 58.75% (3.63kWh) 83.36% 7.22% (0.927kWh) Generation Loss Available Braking energy in theory 7.2% (0.388kWh) Figure 6: Energy flow diagram of the parallel hybrid powertrain in the Chinese typical city bus driving cycle 99% Loss 0.09% (0.008kWh) 00.48% (9.288kWh) 00.4% (9.28kWh) 77.7% (7.33kWh) 00% (9.243kWh) Vehicle Axle 25.% (2.32kWh) Auxiliary Devices Loss (5kW) Efficiency 23.22% 83.63% (2.47kWh) 22.83% (2.kWh) and Controller -driven Loss 27.3% (2.523kWh) Battery 56.8% (5.93kWh) 4.47% (0.43kWh) 49.23% Generation (4.55kWh) Efficiency 6.28% 77.53% (.505kWh) 53.4% Generation Loss (4.936kWh) Available Braking energy in theory 9% Energy Storage System Loss 3.97% (0.367kWh) Figure 7: Energy flow diagram of the parallel hybrid powertrain in the bus driving cycle of Harbin City, Heilongjiang Province As can be seen from Figure 7, regarding the output power of driving the vehicle as the reference value, the theoretical braking energy can reach 53.4%. The efficiency of the engine can reach 34%. The EVS28 International Electric Vehicle Symposium and Exhibition 5

6 driving and generating energy efficiencies of the motor and its controller section are 83.63% and 77.53%, respectively, which still has space to optimized control. The fuel economy of the parallel hybrid electric bus and the conventional fuel bus are compared in Table 3. It includes 2 kinds of urban driving cycles. Table 3: Comparison of the fuel economy between the parallel hybrid electric bus and the conventional fuel bus Conventional Fuel Bus fuel consumption (L/00km) Parallel Hybrid Electric Bus fuel consumption (L/00km) Rate of fuel saving(%) Chinese typical city bus driving cycle Bus driving cycle of Harbin City, Heilongjiang Province As can be seen from the comparison of Table 3, the fuel saving rate of the parallel hybrid electric bus which is operating in China can reach more than 20% in different China urban driving cycles. The rate of fuel saving can reach 2.2% in the Chinese typical city bus driving cycle and 22.69% in the bus driving cycle of Harbin City, Heilongjiang Province. The fuel economy can be improved on the selection and controlling of the energy storage elements, the motor and its controller, and other components. 5 Conclusion Based on the simulation model of the parallel hybrid electric bus which is demonstrating in China, this paper regards the Chinese typical city bus driving cycle and the bus driving cycle of Harbin City, Heilongjiang Province as the driving cycles for the simulation, and analysed the energy efficient and the fuel economy of the parallel hybrid powertrain system. The fuel saving rate of the parallel hybrid electric bus which is operating in China can reach more than 20% in two kinds of China urban bus driving cycles. The rate of fuel saving is higher in the bus driving cycle of Harbin City, Heilongjiang Province, which can reach 22.69%. As the generation efficiency of the motor and the charge and discharge efficiency of the battery are lower in the demonstrating parallel hybrid electric bus, the fuel economy of the hybrid powertrain can be optimized by improving relevant components and the control method. Sources referenced in the paper shall be done with sequential numerical references, between square brackets. Acknowledgments This work is supported by Natural Science Foundation of Heilongjiang Province of China (E2045), and funded by MOST (Ministry of Science and Technology) International S&T Cooperation Program of China under contract of No. 204DFG7590 and 202DFA890, and National Science and Technology Infrastructure Program under contract of No. 203BAG06B04. References [] OUYANG Minggao, LI Jianqiu and YANG Fuyuan, et al. Automotive new powertrain: systems, models and controls[m]. Beijing: Tsinghua University Press, [2] Y. Zhang, H. P. Liu. Fuzzy multi-objective control strategy for parallel hybrid electric vehicle[j]. IET Systems in Transportation, vol.2, no.2, pp.39-50, 202. [3] Mitra Pourabdollah, Nikolce Murgovski and Anders Grauers, et al. Optimal sizing of a parallel PHEV powertrain[j]. IEEE Transactions on Vehicular Technology, vol.62, no.6, pp , 203. [4] Jong-Seob Won, Reza Langari. Intelligent energy management agent for a parallel hybrid vehicle- Part II: torque distribution, charge sustenance strategies, and performance results[j]. IEEE Transactions on Vehicular Technology, vol.54, no.3, pp , [5] Lianghong Wu, Yaonan Wang and Xiaofang Yuan, et al. Multiobjective optimization of HEV fuel economy and emissions using the self-adaptive differential evolution algorithm[j]. IEEE Transactions on Vehicular Technology, vol.60, no.6, pp , 20. [6] Saida Kermani, Rochdi Trigui and Sebastien Delprat, et al. PHIL implementation of energy management optimization for a parallel HEV on a predefined route[j]. IEEE Transactions on Vehicular Technology, vol.60, no.3, pp , 20. [7] Lin He, Tielong Shen and Liangyao Yu, et al. A model-predictive-control based torque demand EVS28 International Electric Vehicle Symposium and Exhibition 6

7 control approach for parallel hybrid powertrains[j]. IEEE Transactions on Vehicular Technology, vol.62, no.3, pp , 203. [8] QIN Datong, YANG Guanlong and LIU Yonggang, et al. Energy management optimization control strategy of plug-in parallel single-motor hybrid electric vehicle[j]. China Journal of Highway and Transport, vol.26, no.5, pp.70-76, 203. [9] HU Yuhui, YANG Lin and XI Junqing, et al. An analysis on the shifting schedule of a single-shaft parallel hybrid electric bus[j]. Automotive ering, vol.35, no.7, pp , 203. [0] Xiaogang WU, Jiuyu DU and Chen HU. Energy efficiency and fuel economy analysis of a series hybrid electric bus in different Chinese city driving cycles[j]. International Journal of Smart Home, vol.7, no.5, pp , 203. [] XIONG Huasheng, CAO Guijun, LU Languang, OUYANG Minggao and LI Jianqiu. Simulation system of series hybrid powertrain for city bus and its applications[j]. Journal of System Simulation, vol.22, no.5, pp.34-38, 200. [2] XIONG Rui, HE Hongwen and ZHANG Xiaowei. Modeling of ultracapacitor based on experimental data[j]. Vehicle and Power Technology, no.4, pp.25-28, 200. [3] Zhisheng Yu. The theory of automobile [M]. Beijing:Machinery Industry Press,2006. Dr. Jiuyu Du is an assistant professor of Tsinghua University. She focuses on advanced vehicle powertrain design, simulation, energy-saving and new energy vehicle system analysis, and performance analysis and evaluation of vehicle powertrain, and electric vehicle R&D technology roadmap. Authors Xiaogang Wu is a Professor of Power Electronics and Power, Harbin University of Science and Technology. He received the B.S., M.S. and Ph.D. degrees in electrical engineering from the Harbin University of Science and Technology His research interest is the powertrain control of electric vehicles. Jingfu Chen is a graduate student of Department of Automation, Harbin University of Science and Technology. He is a member of U.S.-China CERC-clean Vehicles Consortium. His current focus is plug-in electric vehicles and electric vehicle R&D technology roadmap. EVS28 International Electric Vehicle Symposium and Exhibition 7